Implementação de um sistema Collaborative Planning, Forecasting, and Replenishment em uma grande rede de fast food por meio de um prestador de serviços logísticos

A gestão colaborativa é, atualmente, um elemento-chave no contexto da gestão da cadeia de suprimentos. Neste artigo, o tema é abordado mediante a análise de um caso real, em que uma grande rede mundial de fast-food e seu prestador de serviço logístico (PSL) trabalharam conjuntamente no Brasil em um projeto-piloto para a implementação de um collaborative planning, forecasting, and replenishment (CPFR). O trabalho faz uso de uma metodologia de pesquisa-ação e apresenta as principais variáveis que influenciaram o projeto, abordando os processos necessários para a implementação e os pontos que favorecem o CPFR. Com base no caso estudado, o trabalho apresenta um conjunto de propostas sobre o papel dos agentes da cadeia em projetos dessa natureza. A gestão da cadeia de suprimentos por intermédio da coordenação direta de um PSL também permite demonstrar as possibilidades e dificuldades desse sistema, contribuindo com a visão colaborativa na cadeia de suprimentos a partir da relação entre seus agentes.

Sea waves generated by extratropical cyclones in the South Atlantic Ocean: Hindcast and validation against altimeter data

This work presents analyses of the atmospheric conditions and the hindcast of the surface wave field when six extratropical cyclones formed and displaced over the South Atlantic Ocean (10degreesN, 60degreesS; 75degreesW, 15degreesE) between April and September 1999. These events caused high sea waves associated with hazardous conditions along the south and southeast coast of Brazil. The meteorological composite fields for these cyclones show a strong near-surface wind velocity (up to 14 m s(-1)) during its mature phase. The sea-state wave hindcast was obtained using a third-generation wave model forced by the 10-m above ground level wind field from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. Closer to the south and southeast Brazilian coast, the hindcast results showed significant wave heights of up to 5 m in some of the events. The wave hindcast results for the significant wave height were compared against satellite altimeter data at 6 h intervals. The statistical index showed a systematic underestimation of the significant wave height by 0.5 m. The correlation between wave hindcast and altimeter measurements was greater than 90%, showing a good phase reproduction by the wave model.

The use of simulation for forecasting the number of docks necessary in an expedition department

The simulation is a very powerful tool to develop more efficient systems, hence it is been widely used with the goal of productivity improvement. Its results, if compared with other methods, are not always optimum; however, if the experiment is rightly elaborated, its results will represent the real situation, enabling its use with a good level of reliability. This work used the simulation (through the ProModel (R) software) in order to study, understand, model and improve the expenditure system of an enterprise, with a premise of keeping the production-delivery flow considering quick, controlled and reliable conditions.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

A case study of the 9 August 1988 South Atlantic storm: Numerical simulations of the wave activity

During 9-11 August 1988, a cyclone developed over Uruguay in the lee of the Andes Mountains and moved over the South Atlantic Ocean, where it redeveloped into an intense storm. This storm was responsible for unusual wave activity along the Brazilian shoreline from 22° to 32°S. The Brazilian news media reported the loss of at least one life, waves of 3 m and higher, and the disappearance of a drainage pipe, which weighed 8000 kg, off the shores of Rio de Janeiro. In this paper, the evolution of this intense storm and the associated ocean wave response is studied through European Centre for Medium-Range Weather Forecasts analyses, a hydrostatic limited-area meteorological model, and a second-generation prognostic wave model. The atmospheric model results indicated the presence of a long-lived and large fetch with surface wind velocities higher than 12 m s -1 directed toward the coast. Some areas with velocities of 20 m s -1 were embedded in the fetch. The wave model forced by this wind field was able to simulate waves with a significant height of 8 m far from the coast and about 4 m in regions very close to the Brazilian coast in agreement with the occurrence reported at Rio de Janeiro. The swell propagation toward the coast of Rio de Janeiro was obstructed by a northeastward 10-m wind during the first 24-h period of the model's integration. During the second 24-h period, the fetch was still large and strong, but the obstacle was removed by a counterclockwise rotation of wind direction favoring the swell and windsea propagation toward the Rio de Janeiro coast.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

Broiler Response to Different Wind Speeds

Broiler production in Brazil has turned into a very competitive activity in the late years. Constant innovation leads to higher productivity maintaining the same cost of production, which is a desirable situation. Lately one characteristic for broiler housing in Brazil has been the increase in birds density requiring the use of controlled environment through the use of fan and fogging systems in order to achieve better birds productive performance. Most Brazilian producer already uses cooling equipment however it is still unknown the right way to control the wind speed and direction towards the birds. This present research has the objective to evaluate the effect of the wind speed on the heat transfer from the birds to the environment for broilers at 27 days old. There was used 200 birds, placed in a wind tunnel measuring 1.10 m high by 1.10m wide x 10.0 m of length, and the birds density varied from 9, 16 and 20 birds/m 2. Two wind speed were simulated 340 rpm (1.0 m/s) and 250 rpm (0.3 m/s). The increase in the wind velocity related to the smaller bird densityled to a higher heat loss and to a more uniform temperature distribution in its exposed areas.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

A nonlinear and non-ideal wind generator supporting structure

We present a simple mathematical model of a wind turbine supporting tower. Here, the wind excitation is considered to be a non-ideal power source. In such a consideration, there is interaction between the energy supply and the motion of the supporting structure. If power is not enough, the rotation of the generator may get stuck at a resonance frequency of the structure. This is a manifestation of the so-called Sommerfeld Effect. In this model, at first, only two degrees of freedom are considered, the horizontal motion of the upper tip of the tower, in the transverse direction to the wind, and the generator rotation. Next, we add another degree of freedom, the motion of a free rolling mass inside a chamber. Its impact with the walls of the chamber provides control of both the amplitude of the tower vibration and the width of the band of frequencies in which the Sommerfeld effect occur. Some numerical simulations are performed using the equations of motion of the models obtained via a Lagrangian approach.

Evaluating the effects of high penetrations of roof-top wind Turbines on secondary distribution circuits

Incentives for using wind power and the increasing price of energy might generate in a relatively short time a scenario where low voltage customers opt to install roof-top wind turbines. This paper focuses on evaluating the effects of such situation in terms of energy consumption, loss reduction, reverse power flow and voltage profiles. Various commercially-available roof-top wind turbines are installed in two secondary distribution circuits considering real-life wind speed data and seasonal load demand. Results are presented and discussed. © 2006 IEEE.

Towards forecasting and mitigating ionospheric scintillation effects on GNSS

The effect of the ionosphere on the signals of Global Navigation Satellite Systems (GNSS), such as the Global Positionig System (GPS) and the proposed European Galileo, is dependent on the ionospheric electron density, given by its Total Electron Content (TEC). Ionospheric time-varying density irregularities may cause scintillations, which are fluctuations in phase and amplitude of the signals. Scintillations occur more often at equatorial and high latitudes. They can degrade navigation and positioning accuracy and may cause loss of signal tracking, disrupting safety-critical applications, such as marine navigation and civil aviation. This paper addresses the results of initial research carried out on two fronts that are relevant to GNSS users if they are to counter ionospheric scintillations, i.e. forecasting and mitigating their effects. On the forecasting front, the dynamics of scintillation occurrence were analysed during the severe ionospheric storm that took place on the evening of 30 October 2003, using data from a network of GPS Ionospheric Scintillation and TEC Monitor (GISTM) receivers set up in Northern Europe. Previous results [1] indicated that GPS scintillations in that region can originate from ionospheric plasma structures from the American sector. In this paper we describe experiments that enabled confirmation of those findings. On the mitigation front we used the variance of the output error of the GPS receiver DLL (Delay Locked Loop) to modify the least squares stochastic model applied by an ordinary receiver to compute position. This error was modelled according to [2], as a function of the S4 amplitude scintillation index measured by the GISTM receivers. An improvement of up to 21% in relative positioning accuracy was achieved with this technnique.

Spatial electric load forecasting using a local movement approach

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability. © 2009 IEEE.

Spatial load forecasting using a demand propagation approach

A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.